Load data from All.RData

rm(list=ls())  # clean up workspace
load("/Users/Xiang/GitFolders/IGCCodonSimulation/All.RData")

paml.path <- "/Users/Xiang/GitFolders/IGCCodonSimulation/"
IGC.geo.list <- c(3.0)

for (IGC.geo in IGC.geo.list){
  for (localtree in 1:3){
    summary_mat <- NULL
    IGC.geo.path <- paste("IGCgeo_", toString(IGC.geo), ".0/", sep = "")
    file.name <- paste("geo", paste(toString(IGC.geo), ".0", sep = ""), "estimatedTau", "paml", "unrooted", "LocalTree", toString(localtree), "summary.txt", sep = "_")
    for (sim.num in 0:(num.sim - 1)){
      summary_file <- paste(paml.path, file.name, sep = "")
      if (file.exists(summary_file)){
        all <- readLines(summary_file, n = -1)
        col.names <- strsplit(all[1], ' ')[[1]][-1]
        row.names <- strsplit(all[length(all)], ' ')[[1]][-1]
        summary_mat <- as.matrix(read.table(summary_file, 
                                            row.names = row.names, 
                                            col.names = col.names))
        
      }
    }
    assign(paste("PAML", "estimatedTau", paste(toString(IGC.geo), ".0", sep = ""), "LocalTree", toString(localtree), "summary", sep = "_"), summary_mat)}
}


# Read in new PAML results
data.path <- paste(paralog, "",sep = "/")
for (IGC.geo in IGC.geo.list){
  summary_mat <- NULL
  IGC.geo.path <- paste("IGCgeo_", toString(IGC.geo), ".0/", sep = "")
  file.name <- paste("geo", paste(toString(IGC.geo), ".0", sep = ""), "estimatedTau", "paml", "unrooted", "1stTree", "summary.txt", sep = "_")
  for (sim.num in 0:(num.sim - 1)){
    summary_file <- paste(paml.path, file.name, sep = "")
    if (file.exists(summary_file)){
      all <- readLines(summary_file, n = -1)
      col.names <- strsplit(all[1], ' ')[[1]][-1]
      row.names <- strsplit(all[length(all)], ' ')[[1]][-1]
      summary_mat <- as.matrix(read.table(summary_file, 
                                          row.names = row.names, 
                                          col.names = col.names))
      
    }
  }
  assign(paste("PAML", "estimatedTau", paste(toString(IGC.geo), ".0", sep = ""), "1stTree", "summary", sep = "_"), summary_mat)
}

for (IGC.geo in IGC.geo.list){
  summary_mat <- NULL
  IGC.geo.path <- paste("IGCgeo_", toString(IGC.geo), ".0/", sep = "")
  file.name <- paste("geo", paste(toString(IGC.geo), ".0", sep = ""), "estimatedTau", "paml", "unrooted", "2ndTree", "summary.txt", sep = "_")
  for (sim.num in 0:(num.sim - 1)){
    summary_file <- paste(paml.path, file.name, sep = "")
    if (file.exists(summary_file)){
      all <- readLines(summary_file, n = -1)
      col.names <- strsplit(all[1], ' ')[[1]][-1]
      row.names <- strsplit(all[length(all)], ' ')[[1]][-1]
      summary_mat <- as.matrix(read.table(summary_file, 
                                          row.names = row.names, 
                                          col.names = col.names))
      
    }
  }
  assign(paste("PAML", "estimatedTau", paste(toString(IGC.geo), ".0", sep = ""), "2ndTree", "summary", sep = "_"), summary_mat)
}

for (IGC.geo in IGC.geo.list){
  summary_mat <- NULL
  IGC.geo.path <- paste("IGCgeo_", toString(IGC.geo), ".0/", sep = "")
  file.name <- paste("geo", paste(toString(IGC.geo), ".0", sep = ""), "10Tau", "paml", "unrooted", "1stTree", "summary.txt", sep = "_")
  for (sim.num in 0:(num.sim - 1)){
    summary_file <- paste(paml.path, file.name, sep = "")
    if (file.exists(summary_file)){
      all <- readLines(summary_file, n = -1)
      col.names <- strsplit(all[1], ' ')[[1]][-1]
      row.names <- strsplit(all[length(all)], ' ')[[1]][-1]
      summary_mat <- as.matrix(read.table(summary_file, 
                                          row.names = row.names, 
                                          col.names = col.names))
      
    }
  }
  assign(paste("PAML", "10Tau", paste(toString(IGC.geo), ".0", sep = ""), "1stTree", "summary", sep = "_"), summary_mat)
}

for (IGC.geo in IGC.geo.list){
  summary_mat <- NULL
  IGC.geo.path <- paste("IGCgeo_", toString(IGC.geo), ".0/", sep = "")
  file.name <- paste("geo", paste(toString(IGC.geo), ".0", sep = ""), "10Tau", "paml", "unrooted", "2ndTree", "summary.txt", sep = "_")
  for (sim.num in 0:(num.sim - 1)){
    summary_file <- paste(paml.path, file.name, sep = "")
    if (file.exists(summary_file)){
      all <- readLines(summary_file, n = -1)
      col.names <- strsplit(all[1], ' ')[[1]][-1]
      row.names <- strsplit(all[length(all)], ' ')[[1]][-1]
      summary_mat <- as.matrix(read.table(summary_file, 
                                          row.names = row.names, 
                                          col.names = col.names))
      
    }
  }
  assign(paste("PAML", "10Tau", paste(toString(IGC.geo), ".0", sep = ""), "2ndTree", "summary", sep = "_"), summary_mat)
}

save.image("/Users/Xiang/GitFolders/IGCCodonSimulation/All.RData")

Feb 11 update

sum(PAML_estimatedTau_3.0_1stTree_summary["ll",] - PAML_estimatedTau_3.0_LocalTree_1_summary["ll",] < 0)
## [1] 52
sum(PAML_estimatedTau_3.0_1stTree_summary["ll",] - PAML_estimatedTau_3.0_LocalTree_2_summary["ll",] < 0)
## [1] 59
sum(PAML_estimatedTau_3.0_1stTree_summary["ll",] - PAML_estimatedTau_3.0_LocalTree_3_summary["ll",] < 0)
## [1] 33
hist((PAML_estimatedTau_3.0_1stTree_summary["ll",] - PAML_estimatedTau_3.0_LocalTree_1_summary["ll",])[PAML_estimatedTau_3.0_1stTree_summary["ll",] - PAML_estimatedTau_3.0_LocalTree_1_summary["ll",] < 0],
     main = "lnL difference - local tree 1",
     xlab = "lnL difference")

hist((PAML_estimatedTau_3.0_1stTree_summary["ll",] - PAML_estimatedTau_3.0_LocalTree_2_summary["ll",])[PAML_estimatedTau_3.0_1stTree_summary["ll",] - PAML_estimatedTau_3.0_LocalTree_2_summary["ll",] < 0],
     main = "lnL difference - local tree 2",
     xlab = "lnL difference")

hist((PAML_estimatedTau_3.0_1stTree_summary["ll",] - PAML_estimatedTau_3.0_LocalTree_3_summary["ll",])[PAML_estimatedTau_3.0_1stTree_summary["ll",] - PAML_estimatedTau_3.0_LocalTree_3_summary["ll",] < 0],
     main = "lnL difference - local tree 3",
     xlab = "lnL difference")

Feb 10 update

# Re-examine N4_N5, N4_mikatae
IGC.geo.list <- c(3.0, 10.0, 50.0, 100.0, 500.0)

# N4_N5
non.zero.PAML.N4.N5.paralog1.mean.list <- c(mean(PAML_3.0_summary["N4_N5", (PAML_3.0_summary[4, ] > 1e-5 & PAML_3.0_summary[6,] > 1e-5)]), 
                                            mean(PAML_10.0_summary["N4_N5", (PAML_10.0_summary[4, ] > 1e-5 & PAML_10.0_summary[6,] > 1e-5)]),
                                            mean(PAML_50.0_summary["N4_N5", (PAML_50.0_summary[4, ] > 1e-5 & PAML_50.0_summary[6,] > 1e-5)]),
                                            mean(PAML_100.0_summary["N4_N5", (PAML_100.0_summary[4, ] > 1e-5 & PAML_100.0_summary[6,] > 1e-5)]),
                                            mean(PAML_500.0_summary["N4_N5", (PAML_500.0_summary[4, ] > 1e-5 & PAML_500.0_summary[6,] > 1e-5)]))
non.zero.PAML.N4.N5.paralog1.sd.list <- c(sd(PAML_3.0_summary["N4_N5", (PAML_3.0_summary[4, ] > 1e-5 & PAML_3.0_summary[6,] > 1e-5)]), 
                                          sd(PAML_10.0_summary["N4_N5", (PAML_10.0_summary[4, ] > 1e-5 & PAML_10.0_summary[6,] > 1e-5)]),
                                          sd(PAML_50.0_summary["N4_N5", (PAML_50.0_summary[4, ] > 1e-5 & PAML_50.0_summary[6,] > 1e-5)]),
                                          sd(PAML_100.0_summary["N4_N5", (PAML_100.0_summary[4, ] > 1e-5 & PAML_100.0_summary[6,] > 1e-5)]),
                                          sd(PAML_500.0_summary["N4_N5", (PAML_500.0_summary[4, ] > 1e-5 & PAML_500.0_summary[6,] > 1e-5)]))
non.zero.PAML.N4.N5.paralog2.mean.list <- c(mean(PAML_3.0_summary["N9_N10", (PAML_3.0_summary[4, ] > 1e-5 & PAML_3.0_summary[6,] > 1e-5)]), 
                                            mean(PAML_10.0_summary["N9_N10", (PAML_10.0_summary[4, ] > 1e-5 & PAML_10.0_summary[6,] > 1e-5)]),
                                            mean(PAML_50.0_summary["N9_N10", (PAML_50.0_summary[4, ] > 1e-5 & PAML_50.0_summary[6,] > 1e-5)]),
                                            mean(PAML_100.0_summary["N9_N10", (PAML_100.0_summary[4, ] > 1e-5 & PAML_100.0_summary[6,] > 1e-5)]),
                                            mean(PAML_500.0_summary["N9_N10", (PAML_500.0_summary[4, ] > 1e-5 & PAML_500.0_summary[6,] > 1e-5)]))
non.zero.PAML.N4.N5.paralog2.sd.list <- c(sd(PAML_3.0_summary["N9_N10", (PAML_3.0_summary[4, ] > 1e-5 & PAML_3.0_summary[6,] > 1e-5)]), 
                                          sd(PAML_10.0_summary["N9_N10", (PAML_10.0_summary[4, ] > 1e-5 & PAML_10.0_summary[6,] > 1e-5)]),
                                          sd(PAML_50.0_summary["N9_N10", (PAML_50.0_summary[4, ] > 1e-5 & PAML_50.0_summary[6,] > 1e-5)]),
                                          sd(PAML_100.0_summary["N9_N10", (PAML_100.0_summary[4, ] > 1e-5 & PAML_100.0_summary[6,] > 1e-5)]),
                                          sd(PAML_500.0_summary["N9_N10", (PAML_500.0_summary[4, ] > 1e-5 & PAML_500.0_summary[6,] > 1e-5)]))
# N4_mikatae
non.zero.PAML.N4.mikatae.paralog1.mean.list <- c(mean(PAML_3.0_summary["N4_mikataeYDR418W", (PAML_3.0_summary[4, ] > 1e-5 & PAML_3.0_summary[6,] > 1e-5)]), 
                                                 mean(PAML_10.0_summary["N4_mikataeYDR418W", (PAML_10.0_summary[4, ] > 1e-5 & PAML_10.0_summary[6,] > 1e-5)]),
                                                 mean(PAML_50.0_summary["N4_mikataeYDR418W", (PAML_50.0_summary[4, ] > 1e-5 & PAML_50.0_summary[6,] > 1e-5)]),
                                                 mean(PAML_100.0_summary["N4_mikataeYDR418W", (PAML_100.0_summary[4, ] > 1e-5 & PAML_100.0_summary[6,] > 1e-5)]),
                                                 mean(PAML_500.0_summary["N4_mikataeYDR418W", (PAML_500.0_summary[4, ] > 1e-5 & PAML_500.0_summary[6,] > 1e-5)]))
non.zero.PAML.N4.mikatae.paralog1.sd.list <- c(sd(PAML_3.0_summary["N4_mikataeYDR418W", (PAML_3.0_summary[4, ] > 1e-5 & PAML_3.0_summary[6,] > 1e-5)]), 
                                               sd(PAML_10.0_summary["N4_mikataeYDR418W", (PAML_10.0_summary[4, ] > 1e-5 & PAML_10.0_summary[6,] > 1e-5)]),
                                               sd(PAML_50.0_summary["N4_mikataeYDR418W", (PAML_50.0_summary[4, ] > 1e-5 & PAML_50.0_summary[6,] > 1e-5)]),
                                               sd(PAML_100.0_summary["N4_mikataeYDR418W", (PAML_100.0_summary[4, ] > 1e-5 & PAML_100.0_summary[6,] > 1e-5)]),
                                               sd(PAML_500.0_summary["N4_mikataeYDR418W", (PAML_500.0_summary[4, ] > 1e-5 & PAML_500.0_summary[6,] > 1e-5)]))
non.zero.PAML.N4.mikatae.paralog2.mean.list <- c(mean(PAML_3.0_summary["N9_mikataeYEL054C", (PAML_3.0_summary[4, ] > 1e-5 & PAML_3.0_summary[6,] > 1e-5)]), 
                                                 mean(PAML_10.0_summary["N9_mikataeYEL054C", (PAML_10.0_summary[4, ] > 1e-5 & PAML_10.0_summary[6,] > 1e-5)]),
                                                 mean(PAML_50.0_summary["N9_mikataeYEL054C", (PAML_50.0_summary[4, ] > 1e-5 & PAML_50.0_summary[6,] > 1e-5)]),
                                                 mean(PAML_100.0_summary["N9_mikataeYEL054C", (PAML_100.0_summary[4, ] > 1e-5 & PAML_100.0_summary[6,] > 1e-5)]),
                                                 mean(PAML_500.0_summary["N9_mikataeYEL054C", (PAML_500.0_summary[4, ] > 1e-5 & PAML_500.0_summary[6,] > 1e-5)]))
non.zero.PAML.N4.mikatae.paralog2.sd.list <- c(sd(PAML_3.0_summary["N9_mikataeYEL054C", (PAML_3.0_summary[4, ] > 1e-5 & PAML_3.0_summary[6,] > 1e-5)]), 
                                               sd(PAML_10.0_summary["N9_mikataeYEL054C", (PAML_10.0_summary[4, ] > 1e-5 & PAML_10.0_summary[6,] > 1e-5)]),
                                               sd(PAML_50.0_summary["N9_mikataeYEL054C", (PAML_50.0_summary[4, ] > 1e-5 & PAML_50.0_summary[6,] > 1e-5)]),
                                               sd(PAML_100.0_summary["N9_mikataeYEL054C", (PAML_100.0_summary[4, ] > 1e-5 & PAML_100.0_summary[6,] > 1e-5)]),
                                               sd(PAML_500.0_summary["N9_mikataeYEL054C", (PAML_500.0_summary[4, ] > 1e-5 & PAML_500.0_summary[6,] > 1e-5)]))

# N4_N5
par(mfrow = c(2, 2))
matplot(IGC.geo.list, cbind(non.zero.PAML.N4.N5.paralog1.mean.list, non.zero.PAML.N4.N5.paralog2.mean.list, mean.post.N4.N5.list),
        type = c("p"), pch = 1, col = 1:3, ylab = "mean non.zero N4.N5 estimate")
abline(h = 0.024947887926)
legend("right", legend = c("paralog1", "paralog2", "posterior IGC"), col = 1:3, pch = 1)
matplot(IGC.geo.list, cbind(non.zero.PAML.N4.N5.paralog1.sd.list, non.zero.PAML.N4.N5.paralog2.sd.list, sd.post.N4.N5.list),
        type = c("p"), pch = 1, col = 1:3, ylab = "sd non.zero N4.N5 estimate")
legend("right", legend = c("paralog1", "paralog2", "posterior IGC"), col = 1:3, pch = 1)

# N4_N5
matplot(IGC.geo.list, cbind(PAML.N4.N5.paralog1.mean.list, PAML.N4.N5.paralog2.mean.list, mean.post.N4.N5.list),
        type = c("p"), pch = 1, col = 1:3, ylab = "mean N4.N5 estimate")
abline(h = 0.024947887926)
legend("right", legend = c("paralog1", "paralog2", "posterior IGC"), col = 1:3, pch = 1)
matplot(IGC.geo.list, cbind(PAML.N4.N5.paralog1.sd.list, PAML.N4.N5.paralog2.sd.list, sd.post.N4.N5.list),
        type = c("p"), pch = 1, col = 1:3, ylab = "sd N4.N5 estimate")
legend("right", legend = c("paralog1", "paralog2", "posterior IGC"), col = 1:3, pch = 1)

par(mfrow = c(2, 2))
# N4_mikatae
matplot(IGC.geo.list, cbind(non.zero.PAML.N4.mikatae.paralog1.mean.list, non.zero.PAML.N4.mikatae.paralog2.mean.list, mean.post.N4.mikatae.list),
        type = c("p"), pch = 1, col = 1:3, ylab = "mean non.zero N4.mikatae estimate")
abline(h = 0.0566627496729)
legend("right", legend = c("paralog1", "paralog2", "posterior IGC"), col = 1:3, pch = 1)
matplot(IGC.geo.list, cbind(non.zero.PAML.N4.mikatae.paralog1.sd.list, non.zero.PAML.N4.mikatae.paralog2.sd.list, sd.post.N4.mikatae.list),
        type = c("p"), pch = 1, col = 1:3, ylab = "sd non.zero N4.mikatae estimate")
legend("right", legend = c("paralog1", "paralog2", "posterior IGC"), col = 1:3, pch = 1)
# N4_mikatae
matplot(IGC.geo.list, cbind(PAML.N4.mikatae.paralog1.mean.list, PAML.N4.mikatae.paralog2.mean.list, mean.post.N4.mikatae.list),
        type = c("p"), pch = 1, col = 1:3, ylab = "mean N4.mikatae estimate")
abline(h = 0.0566627496729)
legend("right", legend = c("paralog1", "paralog2", "posterior IGC"), col = 1:3, pch = 1)
matplot(IGC.geo.list, cbind(PAML.N4.mikatae.paralog1.sd.list, PAML.N4.mikatae.paralog2.sd.list, sd.post.N4.mikatae.list),
        type = c("p"), pch = 1, col = 1:3, ylab = "sd N4.mikatae estimate")
legend("right", legend = c("paralog1", "paralog2", "posterior IGC"), col = 1:3, pch = 1)

Feb 5 update

Check PAML estimate of two same tree topologies but different order of taxa

# Simulation using estimated Tau value of 1.409408
# Look at difference of each estimate (Tree 1 - Tree 2)

# log likelihood
summary(PAML_estimatedTau_3.0_1stTree_summary["ll", ] - PAML_estimatedTau_3.0_2ndTree_summary["ll", ])
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   0e+00   0e+00   0e+00   1e-08   0e+00   1e-06
sd(PAML_estimatedTau_3.0_1stTree_summary["ll", ] - PAML_estimatedTau_3.0_2ndTree_summary["ll", ])
## [1] 9.999999e-08
# kappa
summary(PAML_estimatedTau_3.0_1stTree_summary["kappa", ] - PAML_estimatedTau_3.0_2ndTree_summary["kappa", ])
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  -2e-05   0e+00   0e+00  -1e-07   0e+00   1e-05
sd(PAML_estimatedTau_3.0_1stTree_summary["kappa", ] - PAML_estimatedTau_3.0_2ndTree_summary["kappa", ])
## [1] 5.221362e-06
# omega
summary(PAML_estimatedTau_3.0_1stTree_summary["omega", ] - PAML_estimatedTau_3.0_2ndTree_summary["omega", ])
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  -1e-05   0e+00   0e+00  -3e-07   0e+00   0e+00
sd(PAML_estimatedTau_3.0_1stTree_summary["omega", ] - PAML_estimatedTau_3.0_2ndTree_summary["omega", ])
## [1] 1.714466e-06
# N0_kluyveriYDR418W
summary(PAML_estimatedTau_3.0_1stTree_summary["N0_kluyveriYDR418W", ] - PAML_estimatedTau_3.0_2ndTree_summary["N0_kluyveriYDR418W", ])
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  -5e-06   0e+00   0e+00  -1e-08   0e+00   1e-06
sd(PAML_estimatedTau_3.0_1stTree_summary["N0_kluyveriYDR418W", ] - PAML_estimatedTau_3.0_2ndTree_summary["N0_kluyveriYDR418W", ])
## [1] 5.772628e-07
# N0_N1
summary(PAML_estimatedTau_3.0_1stTree_summary["N0_N1", ] - PAML_estimatedTau_3.0_2ndTree_summary["N0_N6", ])
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  -1e-06   0e+00   0e+00  -1e-08   0e+00   0e+00
sd(PAML_estimatedTau_3.0_1stTree_summary["N0_N1", ] - PAML_estimatedTau_3.0_2ndTree_summary["N0_N6", ])
## [1] 1e-07

There seems no difference between two different tree representations

Now check PAML results on simulation with 10*Tau

# Simulation using 10 * Tau value of 1.409408 * 10 = 14.09408
# Look at difference of each estimate (Tree 1 - Tree 2)

# log likelihood
summary(PAML_10Tau_3.0_1stTree_summary["ll", ] - PAML_10Tau_3.0_2ndTree_summary["ll", ])
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
## -7.7390  0.0000  0.0000 -0.2247  0.0000  5.1960
sd(PAML_10Tau_3.0_1stTree_summary["ll", ] - PAML_10Tau_3.0_2ndTree_summary["ll", ])
## [1] 1.69195
hist(PAML_10Tau_3.0_1stTree_summary["ll", ] - PAML_10Tau_3.0_2ndTree_summary["ll", ])

# kappa
summary(PAML_10Tau_3.0_1stTree_summary["kappa", ] - PAML_10Tau_3.0_2ndTree_summary["kappa", ])
##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
## -0.99080  0.00000  0.00000 -0.06628  0.00000  0.19920
sd(PAML_10Tau_3.0_1stTree_summary["kappa", ] - PAML_10Tau_3.0_2ndTree_summary["kappa", ])
## [1] 0.1903289
# omega
summary(PAML_10Tau_3.0_1stTree_summary["omega", ] - PAML_10Tau_3.0_2ndTree_summary["omega", ])
##       Min.    1st Qu.     Median       Mean    3rd Qu.       Max. 
## -0.1325000  0.0000000  0.0000000 -0.0000435  0.0000000  0.0589100
sd(PAML_10Tau_3.0_1stTree_summary["omega", ] - PAML_10Tau_3.0_2ndTree_summary["omega", ])
## [1] 0.01822983
# N0_kluyveriYDR418W
summary(PAML_10Tau_3.0_1stTree_summary["N0_kluyveriYDR418W", ] - PAML_10Tau_3.0_2ndTree_summary["N0_kluyveriYDR418W", ])
##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
## -0.18970  0.00000  0.00000 -0.02625  0.00000  0.00000
sd(PAML_10Tau_3.0_1stTree_summary["N0_kluyveriYDR418W", ] - PAML_10Tau_3.0_2ndTree_summary["N0_kluyveriYDR418W", ])
## [1] 0.05795811
# N0_N1
summary(PAML_10Tau_3.0_1stTree_summary["N0_N1", ] - PAML_10Tau_3.0_2ndTree_summary["N0_N6", ])
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##       0       0       0       0       0       0
sd(PAML_10Tau_3.0_1stTree_summary["N0_N1", ] - PAML_10Tau_3.0_2ndTree_summary["N0_N6", ])
## [1] 0
# N1_N2
summary(PAML_10Tau_3.0_1stTree_summary["N1_N2", ] - PAML_10Tau_3.0_2ndTree_summary["N6_N7", ])
##      Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
## -0.388500  0.000000  0.000000  0.003649  0.051540  0.233900
sd(PAML_10Tau_3.0_1stTree_summary["N1_N2", ] - PAML_10Tau_3.0_2ndTree_summary["N6_N7", ])
## [1] 0.1072574
hist(PAML_10Tau_3.0_1stTree_summary["N1_N2", ] - PAML_10Tau_3.0_2ndTree_summary["N6_N7", ])

# N1_castelliiYDR418W
summary(PAML_10Tau_3.0_1stTree_summary["N1_castelliiYDR418W", ] - PAML_10Tau_3.0_2ndTree_summary["N6_castelliiYDR418W", ])
##      Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
## -0.233900 -0.051540  0.000000 -0.006778  0.000000  0.388500
sd(PAML_10Tau_3.0_1stTree_summary["N1_castelliiYDR418W", ] - PAML_10Tau_3.0_2ndTree_summary["N6_castelliiYDR418W", ])
## [1] 0.1068431
hist(PAML_10Tau_3.0_1stTree_summary["N1_castelliiYDR418W", ] - PAML_10Tau_3.0_2ndTree_summary["N6_castelliiYDR418W", ])

# N2_N3
summary(PAML_10Tau_3.0_1stTree_summary["N2_N3", ] - PAML_10Tau_3.0_2ndTree_summary["N7_N8", ])
##      Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
## -0.076140 -0.017320  0.000000 -0.007543  0.000000  0.054660
sd(PAML_10Tau_3.0_1stTree_summary["N2_N3", ] - PAML_10Tau_3.0_2ndTree_summary["N7_N8", ])
## [1] 0.02131937
hist(PAML_10Tau_3.0_1stTree_summary["N2_N3", ] - PAML_10Tau_3.0_2ndTree_summary["N7_N8", ])
# N2_N3
summary(PAML_10Tau_3.0_1stTree_summary["N2_N3", ] - PAML_10Tau_3.0_2ndTree_summary["N7_N8", ])
##      Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
## -0.076140 -0.017320  0.000000 -0.007543  0.000000  0.054660
sd(PAML_10Tau_3.0_1stTree_summary["N2_N3", ] - PAML_10Tau_3.0_2ndTree_summary["N7_N8", ])
## [1] 0.02131937
hist(PAML_10Tau_3.0_1stTree_summary["N2_N3", ] - PAML_10Tau_3.0_2ndTree_summary["N7_N8", ])

# N2_bayanusYDR418W
summary(PAML_10Tau_3.0_1stTree_summary["N2_bayanusYDR418W", ] - PAML_10Tau_3.0_2ndTree_summary["N7_bayanusYDR418W", ])
##       Min.    1st Qu.     Median       Mean    3rd Qu.       Max. 
## -0.0546600 -0.0000385  0.0000000  0.0071180  0.0173400  0.0761400
sd(PAML_10Tau_3.0_1stTree_summary["N2_bayanusYDR418W", ] - PAML_10Tau_3.0_2ndTree_summary["N7_bayanusYDR418W", ])
## [1] 0.02080385
hist(PAML_10Tau_3.0_1stTree_summary["N2_bayanusYDR418W", ] - PAML_10Tau_3.0_2ndTree_summary["N7_bayanusYDR418W", ])

Now start to check branches ratio of subtree branches of paralog 1 over paralog 2 ratios

# N0_N1
target <- PAML_3.0_summary["N0_N1", ] / PAML_3.0_summary["N0_N6", ]
hist(target, main = "N0_N1"); mean(target); sd(target)

## [1] 682.5957
## [1] 1311.021
# N1_N2
target <- PAML_3.0_summary["N1_N2", ] / PAML_3.0_summary["N6_N7", ]
hist(target, main = "N1_N2"); mean(target); sd(target)

## [1] 1.009262
## [1] 0.2219263
# N1_castellii
target <- PAML_3.0_summary["N1_castelliiYDR418W", ] / PAML_3.0_summary["N6_castelliiYEL054C", ]
hist(target, main = "N1_castellii"); mean(target); sd(target)

## [1] 1.010218
## [1] 0.2422185
# N2_N3
target <- PAML_3.0_summary["N2_N3", ] / PAML_3.0_summary["N7_N8", ]
hist(target, main = "N2_N3"); mean(target); sd(target)

## [1] 396.4708
## [1] 1982.431
# N2_bayanus
target <- PAML_3.0_summary["N2_bayanusYDR418W", ] / PAML_3.0_summary["N7_bayanusYEL054C", ]
hist(target, main = "N2_bayanus"); mean(target); sd(target)

## [1] 1.135669
## [1] 0.6685038
# N3_N4
target <- PAML_3.0_summary["N3_N4", ] / PAML_3.0_summary["N8_N9", ]
hist(target, main = "N3_N4"); mean(target); sd(target)

## [1] 1.500644
## [1] 1.810983
# N3_kudriavzevii
target <- PAML_3.0_summary["N3_kudriavzeviiYDR418W", ] / PAML_3.0_summary["N8_kudriavzeviiYEL054C", ]
hist(target, main = "N3_kudriavzevii"); mean(target); sd(target)

## [1] 1.039909
## [1] 0.32901
# N4_N5
target <- PAML_3.0_summary["N4_N5", ] / PAML_3.0_summary["N9_N10", ]
hist(target, main = "N4_N5"); mean(target); sd(target)

## [1] 0.9775722
## [1] 1.313046
# N4_mikatae
target <- PAML_3.0_summary["N4_mikataeYDR418W", ] / PAML_3.0_summary["N9_mikataeYEL054C", ]  # paralog 1 / paralog 2
hist(target, main = "N4_mikatae"); mean(target); sd(target)

## [1] 1.866805
## [1] 1.963559
# N5_paradoxus
target <- PAML_3.0_summary["N5_paradoxusYDR418W", ] / PAML_3.0_summary["N10_paradoxusYEL054C", ]  # paralog 1 / paralog 2
hist(target, main = "N5_paradoxus"); mean(target); sd(target)

## [1] 1.377084
## [1] 1.532407
# N5_cerevisiae
target <- PAML_3.0_summary["N5_cerevisiaeYDR418W", ] / PAML_3.0_summary["N10_cerevisiaeYEL054C", ]  # paralog 1 / paralog 2
hist(target, main = "N5_cerevisiae"); mean(target); sd(target)

## [1] 1.041587
## [1] 0.5020038
# N0_N1
target <- PAML_10.0_summary["N0_N1", ] / PAML_10.0_summary["N0_N6", ]
hist(target, main = "N0_N1"); mean(target); sd(target)

## [1] 808.1555
## [1] 1662.984
# N1_N2
target <- PAML_10.0_summary["N1_N2", ] / PAML_10.0_summary["N6_N7", ]
hist(target, main = "N1_N2"); mean(target); sd(target)

## [1] 1.071101
## [1] 0.281692
# N1_castellii
target <- PAML_10.0_summary["N1_castelliiYDR418W", ] / PAML_10.0_summary["N6_castelliiYEL054C", ]
hist(target, main = "N1_castellii"); mean(target); sd(target)

## [1] 1.025923
## [1] 0.3345364
# N2_N3
target <- PAML_10.0_summary["N2_N3", ] / PAML_10.0_summary["N7_N8", ]
hist(target, main = "N2_N3"); mean(target); sd(target)

## [1] 161.1146
## [1] 1169.193
# N2_bayanus
target <- PAML_10.0_summary["N2_bayanusYDR418W", ] / PAML_10.0_summary["N7_bayanusYEL054C", ]
hist(target, main = "N2_bayanus"); mean(target); sd(target)

## [1] 32.60408
## [1] 313.9553
# N3_N4
target <- PAML_10.0_summary["N3_N4", ] / PAML_10.0_summary["N8_N9", ]
hist(target, main = "N3_N4"); mean(target); sd(target)

## [1] 1.538383
## [1] 1.689708
# N3_kudriavzevii
target <- PAML_10.0_summary["N3_kudriavzeviiYDR418W", ] / PAML_10.0_summary["N8_kudriavzeviiYEL054C", ]
hist(target, main = "N3_kudriavzevii"); mean(target); sd(target)

## [1] 1.1444
## [1] 0.4797683
# N4_N5
target <- PAML_10.0_summary["N4_N5", ] / PAML_10.0_summary["N9_N10", ]
hist(target, main = "N4_N5"); mean(target); sd(target)

## [1] 211.0151
## [1] 1481.767
# N4_mikatae
target <- PAML_10.0_summary["N4_mikataeYDR418W", ] / PAML_10.0_summary["N9_mikataeYEL054C", ]  # paralog 1 / paralog 2
hist(target, main = "N4_mikatae"); mean(target); sd(target)

## [1] 113.2484
## [1] 1108.817
# N5_paradoxus
target <- PAML_10.0_summary["N5_paradoxusYDR418W", ] / PAML_10.0_summary["N10_paradoxusYEL054C", ]  # paralog 1 / paralog 2
hist(target, main = "N5_paradoxus"); mean(target); sd(target)

## [1] 1.290973
## [1] 1.112757
# N5_cerevisiae
target <- PAML_10.0_summary["N5_cerevisiaeYDR418W", ] / PAML_10.0_summary["N10_cerevisiaeYEL054C", ]  # paralog 1 / paralog 2
hist(target, main = "N5_cerevisiae"); mean(target); sd(target)

## [1] 1.040702
## [1] 0.568822
# N0_N1
target <- PAML_50.0_summary["N0_N1", ] / PAML_50.0_summary["N0_N6", ]
hist(target, main = "N0_N1"); mean(target); sd(target)

## [1] 543.9516
## [1] 1426.697
# N1_N2
target <- PAML_50.0_summary["N1_N2", ] / PAML_50.0_summary["N6_N7", ]
hist(target, main = "N1_N2"); mean(target); sd(target)

## [1] 1.073097
## [1] 0.2957784
# N1_castellii
target <- PAML_50.0_summary["N1_castelliiYDR418W", ] / PAML_50.0_summary["N6_castelliiYEL054C", ]
hist(target, main = "N1_castellii"); mean(target); sd(target)

## [1] 1.020654
## [1] 0.2728166
# N2_N3
target <- PAML_50.0_summary["N2_N3", ] / PAML_50.0_summary["N7_N8", ]
hist(target, main = "N2_N3"); mean(target); sd(target)

## [1] 1.352344
## [1] 1.576323
# N2_bayanus
target <- PAML_50.0_summary["N2_bayanusYDR418W", ] / PAML_50.0_summary["N7_bayanusYEL054C", ]
hist(target, main = "N2_bayanus"); mean(target); sd(target)

## [1] 1.459676
## [1] 1.934382
# N3_N4
target <- PAML_50.0_summary["N3_N4", ] / PAML_50.0_summary["N8_N9", ]
hist(target, main = "N3_N4"); mean(target); sd(target)

## [1] 43.96184
## [1] 424.0525
# N3_kudriavzevii
target <- PAML_50.0_summary["N3_kudriavzeviiYDR418W", ] / PAML_50.0_summary["N8_kudriavzeviiYEL054C", ]
hist(target, main = "N3_kudriavzevii"); mean(target); sd(target)

## [1] 1.166184
## [1] 0.7123563
# N4_N5
target <- PAML_50.0_summary["N4_N5", ] / PAML_50.0_summary["N9_N10", ]
hist(target, main = "N4_N5"); mean(target); sd(target)

## [1] 1.407367
## [1] 1.702416
# N4_mikatae
target <- PAML_50.0_summary["N4_mikataeYDR418W", ] / PAML_50.0_summary["N9_mikataeYEL054C", ]  # paralog 1 / paralog 2
hist(target, main = "N4_mikatae"); mean(target); sd(target)

## [1] 186.1275
## [1] 1843.195
# N5_paradoxus
target <- PAML_50.0_summary["N5_paradoxusYDR418W", ] / PAML_50.0_summary["N10_paradoxusYEL054C", ]  # paralog 1 / paralog 2
hist(target, main = "N5_paradoxus"); mean(target); sd(target)

## [1] 101.6888
## [1] 572.1677
# N5_cerevisiae
target <- PAML_50.0_summary["N5_cerevisiaeYDR418W", ] / PAML_50.0_summary["N10_cerevisiaeYEL054C", ]  # paralog 1 / paralog 2
hist(target, main = "N5_cerevisiae"); mean(target); sd(target)

## [1] 1.336023
## [1] 0.9247196
# N0_N1
target <- PAML_100.0_summary["N0_N1", ] / PAML_100.0_summary["N0_N6", ]
hist(target, main = "N0_N1"); mean(target); sd(target)

## [1] 702.8846
## [1] 1764.081
# N1_N2
target <- PAML_100.0_summary["N1_N2", ] / PAML_100.0_summary["N6_N7", ]
hist(target, main = "N1_N2"); mean(target); sd(target)

## [1] 1.049549
## [1] 0.3781572
# N1_castellii
target <- PAML_100.0_summary["N1_castelliiYDR418W", ] / PAML_100.0_summary["N6_castelliiYEL054C", ]
hist(target, main = "N1_castellii"); mean(target); sd(target)

## [1] 1.032278
## [1] 0.2838787
# N2_N3
target <- PAML_100.0_summary["N2_N3", ] / PAML_100.0_summary["N7_N8", ]
hist(target, main = "N2_N3"); mean(target); sd(target)

## [1] 89.56181
## [1] 539.3573
# N2_bayanus
target <- PAML_100.0_summary["N2_bayanusYDR418W", ] / PAML_100.0_summary["N7_bayanusYEL054C", ]
hist(target, main = "N2_bayanus"); mean(target); sd(target)

## [1] 1.386252
## [1] 1.510943
# N3_N4
target <- PAML_100.0_summary["N3_N4", ] / PAML_100.0_summary["N8_N9", ]
hist(target, main = "N3_N4"); mean(target); sd(target)

## [1] 888.6468
## [1] 5070.871
# N3_kudriavzevii
target <- PAML_100.0_summary["N3_kudriavzeviiYDR418W", ] / PAML_100.0_summary["N8_kudriavzeviiYEL054C", ]
hist(target, main = "N3_kudriavzevii"); mean(target); sd(target)

## [1] 1.237806
## [1] 0.9738664
# N4_N5
target <- PAML_100.0_summary["N4_N5", ] / PAML_100.0_summary["N9_N10", ]
hist(target, main = "N4_N5"); mean(target); sd(target)

## [1] 1.44583
## [1] 3.041035
# N4_mikatae
target <- PAML_100.0_summary["N4_mikataeYDR418W", ] / PAML_100.0_summary["N9_mikataeYEL054C", ]  # paralog 1 / paralog 2
hist(target, main = "N4_mikatae"); mean(target); sd(target)

## [1] 2.461894
## [1] 3.708844
# N5_paradoxus
target <- PAML_100.0_summary["N5_paradoxusYDR418W", ] / PAML_100.0_summary["N10_paradoxusYEL054C", ]  # paralog 1 / paralog 2
hist(target, main = "N5_paradoxus"); mean(target); sd(target)

## [1] 845.0949
## [1] 7179.084
# N5_cerevisiae
target <- PAML_100.0_summary["N5_cerevisiaeYDR418W", ] / PAML_100.0_summary["N10_cerevisiaeYEL054C", ]  # paralog 1 / paralog 2
hist(target, main = "N5_cerevisiae"); mean(target); sd(target)

## [1] 1.472391
## [1] 1.872967
# N0_N1
target <- PAML_500.0_summary["N0_N1", ] / PAML_500.0_summary["N0_N6", ]
hist(target, main = "N0_N1"); mean(target); sd(target)

## [1] 390.7935
## [1] 1292.949
# N1_N2
target <- PAML_500.0_summary["N1_N2", ] / PAML_500.0_summary["N6_N7", ]
hist(target, main = "N1_N2"); mean(target); sd(target)

## [1] 1219.064
## [1] 8571.207
# N1_castellii
target <- PAML_500.0_summary["N1_castelliiYDR418W", ] / PAML_500.0_summary["N6_castelliiYEL054C", ]
hist(target, main = "N1_castellii"); mean(target); sd(target)

## [1] 1.067131
## [1] 0.2984771
# N2_N3
target <- PAML_500.0_summary["N2_N3", ] / PAML_500.0_summary["N7_N8", ]
hist(target, main = "N2_N3"); mean(target); sd(target)

## [1] 1309.071
## [1] 6196.195
# N2_bayanus
target <- PAML_500.0_summary["N2_bayanusYDR418W", ] / PAML_500.0_summary["N7_bayanusYEL054C", ]
hist(target, main = "N2_bayanus"); mean(target); sd(target)

## [1] 547.7266
## [1] 4709.178
# N3_N4
target <- PAML_500.0_summary["N3_N4", ] / PAML_500.0_summary["N8_N9", ]
hist(target, main = "N3_N4"); mean(target); sd(target)

## [1] 605.8042
## [1] 2709.521
# N3_kudriavzevii
target <- PAML_500.0_summary["N3_kudriavzeviiYDR418W", ] / PAML_500.0_summary["N8_kudriavzeviiYEL054C", ]
hist(target, main = "N3_kudriavzevii"); mean(target); sd(target)

## [1] 1.347045
## [1] 1.256499
# N4_N5
target <- PAML_500.0_summary["N4_N5", ] / PAML_500.0_summary["N9_N10", ]
hist(target, main = "N4_N5"); mean(target); sd(target)

## [1] 1018.097
## [1] 9689.183
# N4_mikatae
target <- PAML_500.0_summary["N4_mikataeYDR418W", ] / PAML_500.0_summary["N9_mikataeYEL054C", ]  # paralog 1 / paralog 2
hist(target, main = "N4_mikatae"); mean(target); sd(target)

## [1] 722.6354
## [1] 3206.462
# N5_paradoxus
target <- PAML_500.0_summary["N5_paradoxusYDR418W", ] / PAML_500.0_summary["N10_paradoxusYEL054C", ]  # paralog 1 / paralog 2
hist(target, main = "N5_paradoxus"); mean(target); sd(target)

## [1] 140.7491
## [1] 824.1837
# N5_cerevisiae
target <- PAML_500.0_summary["N5_cerevisiaeYDR418W", ] / PAML_500.0_summary["N10_cerevisiaeYEL054C", ]  # paralog 1 / paralog 2
hist(target, main = "N5_cerevisiae"); mean(target); sd(target)

## [1] 1.35265
## [1] 1.520308